Dental Insurance Fraud Dental Insurance Fraud DetectionDetection

With With

Predictive ModelingPredictive ModelingSOA Spring Health Meeting

May 29, 2008

Jonathan Polon FSA

www.claimanalytics.com

• Intro to Predictive Modeling

• Supervised vs Unsupervised Learning

• Modeling Procedures

• Dental Insurance Fraud Detection

• Examples of Atypical Dentists

AgendaAgenda

Introduction to Introduction to Predictive ModelingPredictive Modeling

A Part of Everyday LifeA Part of Everyday LifeHave you used a predictive model

today?

•Mail sorting

•Credit card processing

•Credit scores

•Weather forecasting

•Grocery shopping

What is Predictive What is Predictive ModelingModeling

• Harnesses power of modern computers to find hidden patterns in data

• Used extensively in industry

• Many possible uses in insurance:

•Claim management•Pricing•Reserving•Fraud detection

Predictive Modeling Predictive Modeling ToolsTools

Some common techniques

• Generalized linear models

• Neural networks

• Genetic algorithms

• Random forests

• Stochastic gradient boosted trees

• Support vector machines

SupervisedSupervisedVsVs

Unsupervised Unsupervised LearningLearning

Supervised LearningSupervised Learning

• In the training dataset, there is a known outcome associated with each record

• Learning objective: build a model that can accurately estimate the outcomes from the predictor variables for each record

• Eg, Sports Betting: predict winners based on observations from past games

• Commonly referred to as predictive modeling

Unsupervised LearningUnsupervised Learning

• In the training dataset, there is not an outcome associated with any record

• Learning objective: self-organization or clustering; finding structure in the data.

• Eg, Grocery Stores: define types of shoppers and their preferences

ModelingModelingDentalDental

ProceduresProcedures

Why Model Procedures?Why Model Procedures?

There’s more to diagnosis than category

• within categories, severity varies

• similarities can exist between procedures of different categories

• how do we extract more information?

Scoring ProceduresScoring Procedures

Create a series of metrics for each procedure

• some relative values from 0 – 10

•Some absolute values

• for example:- unbundling - specialized- major problem - emergency

• allows every procedure to be compared to every other procedure

Scoring Procedures - Scoring Procedures - ExampleExample

Caries, trauma,

pain control

Pulpotomy, molars,

emergency

Root canals, 3 canals,

calcified

Category Restorative

Endodontics

Endodontics

Specialized

0 0 8

Major work

5 8 10

Emergency No Yes No

Dental Fraud DetectionDental Fraud DetectionUsingUsing

Unsupervised LearningUnsupervised Learning

Research ProjectResearch Project

• 200,000 dental claim records from 2004

• General practitioners in Ontario

• Applied modern pattern detection techniques to identify dentists with atypical claims histories

• Research paper: Dental Insurance Claims: Identification of Atypical Claims Activity

http://www.actuaries.ca/members/publications/2007/207033e.pdf

Traditional ToolsTraditional Tools

Rule-based

Strong at identifying claims that match known types of fraudulent activity

Limited to identifying what is known

Typically analyze at the level of a single claim, in isolation

Pattern Detection ToolsPattern Detection Tools

• Analyze millions of claims to reveal patterns– Technology learns what is normal and what is

atypical – not limited by what we already know

• Categorize each claim, reveals dentists with a high percentage of atypical claims– Immediately highlights new questionable behaviors

– Finds new large dollar schemes

– Also identifies frequent repetition of small dollar abuses

Methodology – 3 Methodology – 3 PerspectivesPerspectives1. By Claim e.g. Joe Green’s semi-annual

check-up

2. By Tooth e.g. all work done in 2004 on Joe’s bicuspid by Dr. Brown

3. By General Work  e.g. all general work (exams, fluoride, radiographs, scaling and polishing) done on Joe in 2004 by Dr. Brown

Methodology – 2 Methodology – 2 TechniquesTechniques1. Principal components analysis

2. Clustering

PCA Cluster

Claim by claim Tooth by tooth General work

Methodology – SummaryMethodology – Summary

Principal Components Principal Components AnalysisAnalysis•Allows reduction of multi-dimensional data to lower dimensions while maximizing amount of information preserved

•Reducing to 2 dimensions allows data points to be plotted on a chart

•Powerful approach for identifying outliers

PCA: Simple ExamplePCA: Simple ExampleP C A: S am p le D ata

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

X -Ax is

Y-A

xis

Lots of variance between points around both axes

PCA: Simple ExamplePCA: Simple ExampleP C A: Ro ta te Axes 45 o

-6

-4

-2

0

2

4

6

-6 -4 -2 0 2 4 6

X -Ax is

Y-A

xis

Create new axes, X’ and Y’: rotate original axes 45o

PCA: Simple ExamplePCA: Simple Example

In the new axes, very little variance around Y’Y’ contains little “information”

P C A: Ro ta ted Axes

-6

-4

-2

0

2

4

6

-8 -6 -4 -2 0 2 4 6 8

X '-Ax is

Y'-

Ax

is

PCA: Simple ExamplePCA: Simple Example

Can set all Y’ values to 0 – ie, ignore Y’ axisResult: reduce to 1 dimension with little loss of info

P C A: Red u ce to 1 D im en s io n

-6

-4

-2

0

2

4

6

-8 -6 -4 -2 0 2 4 6 8

X '-Ax is

Y'-

Ax

is

♦ Original

● Revised

PCA: Identifying Atypical PCA: Identifying Atypical DentistsDentists

PCA: Identifying Atypical PCA: Identifying Atypical DentistsDentists

1. Begin at the individual transaction level

2. Determine the average transaction for each dentist

3. Graph quickly isolates dentists that are outliers

Dentist Average 90th percentile

PCA: Identifying Atypical PCA: Identifying Atypical DentistsDentists

PCA: SummaryPCA: Summary

1. Visualization allows for easy and intuitive identification of dentists that are atypical

2. Manual investigation required to understand why a dentist or claim is atypical

Clustering Techniques

•Categorization tools

•Organize claims into several groups of similar claims

•Applicable for profiling dentists by looking at the percentage of claims in each cluster

•We apply two different clustering techniques: K-Means and Expectation-Maximization

Clustering ExampleClustering Example

Clustering ExampleClustering Example

Example: Clusters found – by Example: Clusters found – by ClaimClaim 1. Major dental problems

2. Moderate dental problems

3. Age > 28, minor work performed

4. Work includes unbundled procedures

5. Minor work and expensive technologies

6. Age < 28, minor work performed

Clusters: Identifying Atypical Clusters: Identifying Atypical DentistsDentists

1. Begin at the individual claim level

2. Calculate the proportion of claims in each cluster – in total, and for each dentist

3. Isolate dentists with large deviations from average

Clusters: Identifying Atypical Clusters: Identifying Atypical DentistsDentists

Proportion of General Work by ClusterK-Means Clustering

0%

20%

40%

60%

80%

100%

% C

laim

s X

Average 19% 2% 6% 20% 6% 9% 21% 17%

Dent A 0% 0% 0% 0% 0% 49% 0% 51%

1 2 3 4 5 6 7 8

Clustering Techniques: Clustering Techniques: SummarySummary1. Clustering provides an easy to

understand method to profile dentists

2. Unlike PCA, clustering tells why a given dentist is considered atypical

3. Effectively identifies atypical activity at the dentist level

Pilot Project ResultsPilot Project Results

• PCA identified 68 dentists that are atypical

• Clusters identified 182 dentists that are atypical

• 36 dentists are identified as atypical by both PCA and Clusters

• In total, 214 of 1,644 dentists are identified as atypical (13%)

• Billings by atypical dentists were $2.5 MM out of $16.1 MM billed by all dentists (15%)

ExamplesExamplesOfOf

Atypical DentistsAtypical Dentists

Proportion of General Work by ClusterK-Means Clustering

0%

20%

40%

60%

80%

100%%

Cla

ims

X

Average 19% 2% 6% 20% 6% 9% 21% 17%

Dent A 0% 0% 0% 0% 0% 49% 0% 51%

1 2 3 4 5 6 7 8

Analysis: Dentist A is far beyond norms in clusters 6 and 8; both indicate high charges in ‘general dentistry’

What we discovered: Each of Dentist A’s patients is being billed for at least 30 minutes of polishing and 45 minutes of scaling

Dentist ADentist A

Dentist BDentist B

Proportion of Teeth by ClusterK-Means Clustering

0%

20%

40%

60%

80%

100%%

Cla

ims

X

Average 22% 9% 5% 13% 19% 25% 8%

Dent B 5% 20% 21% 0% 0% 0% 54%

1 2 3 4 5 6 7

Analysis : Dentist B is far beyond norms in cluster 7 (age > 19, major work)

What we discovered: Frequent extractions and anesthesia Dentist B looks like an oral surgeon, but is a general practitioner

Dentist CDentist C

Proportion of General Work by ClusterEM Clustering

0%

20%

40%

60%

80%

100%%

Cla

ims

X

Average 23% 77%

Dent C 66% 34%

1 2

Analysis: Very high proportion in Cluster 1, suggesting many high-ticket visits

What we discovered: First, Dentist C performs an inordinate amount of scaling (average of 1 hour p.p.). Second, Dentist C has emergency examinations with atypically high frequency

Dentist MDentist M

Analysis: Dentist M has a disproportionate amount of work beyond the 90th percentile of work by all dentists on all teeth

What we discovered: Dentist M appears to utilize lab work very heavily

More Atypical DentistsMore Atypical Dentists

• Frequent use of panoramic x-rays

• Frequent use of nitrous oxide – including with procedures rarely associated with anesthesia

• Large number of extractions, often using multiple types of sedation

• Very high proportion of claims for crowns and endodontic work

• Individual instruction on oral hygiene provided with very atypical frequency

Dental Fraud SummaryDental Fraud Summary

• Highly effective in identifying dentists with claim portfolios significantly different from the norm

• Enables experts to quickly identify and focus on those dentists with atypical claims activity

Pattern detection:

Questions?Questions?